Utilizing Unsupervised Learning For Data Science

Unsupervised Learning is a powerful source of computing in the current data science online course. The UL models don’t need any kind of intervention from supervisors and therefore it allows the ML models to work independently with a sense of another AI technique that has caught the attention of data engineers. It’s called ‘Augmented Intelligence’ or augmented learning.

Why Focus on Unsupervised Learning?

In modern data science projects, Unsupervised Learning is highly valued as a branch of advanced Machine Learning models that can be used to unravel hidden insights and draw conclusive inferences from Big Data databases without any or with minimal human intervention. These lead to finding advanced applications for ML models such as Clustering that is used in data exploration and grouping.

Here are the top applications of Unsupervised Learning built on Clustering methodology:

K Means: Also called K Medoids clustering, it involves vector quantization in signal processing. Different k sets can be segmented based on spatial differences, or distancing.

Hierarchical clustering: Also called a Cluster tree algorithm, it involves the segmentation and grouping of various objects based on similar and distinct properties.

Gaussian mixture models:

If you are training ML models for probability science, this is the best and most potent algorithm in Clustering. It is used to represent distributed subpopulations within a distinct group of objects. GMMs are used to train ML models when it comes to sub-dividing data points automatically using density based spatial clustering (DBSCAN).

It works best with Gaussian Distributions, also called Normal Distributions. In statistics, you will learn how the various few Gaussian distributions work in practical life.

Self-organizing maps: Also referred to as SOM, the mapping technique uses an advanced NN model to build a grid of neurons or nodes to adapt to the shape of the dataset. It is useful in the data visualization of large data sets and identifies the clustering method that can be deployed for further operations.

Other popular methods include Spectral clustering, Fuzzy Logic, Supervised Clustering, and Centroids or Partitions.

Depending on the level of maturity of your ML model, you can start training your unsupervised model from supervised or semi-supervised features. The use of semi-supervised eases the challenges faced in the clustering process with respect to data labeling. It is very important to identify these challenges in data labeling before the start of your data science online course project. Differentiating between labeled and unlabeled data helps build a better cluster in the subsequent ML modeling.

When to Apply or Leverage Unsupervised Learning in Data Science?

Unsupervised learning is typically deployed to identify features in exploratory data analysis and establish clustering in NLP or NN projects. While k-means and hierarchical clustering are promoted extensively, other techniques are also equally important, especially if you are working with Heat Maps, Location data analytics, sales forecasting, Marketing intelligence, or customer journey intelligence for e-commerce shopping patterns. Another important application of the data science focused ML model is in compressing data.

When we are migrating data and IT resources to Cloud or Virtual storage points, unsupervised ML models are very useful.

Data Compression Using Unsupervised ML Models

I have taken this example as it applies to any data compression operation in the IT domain. ML models in unsupervised format provide the basic foundation for scalable and lossless data compression. You need to have experience in Python and Deep Learning methodologies to handle practical compression with variable/ bit-back coding and asymmetric numeral systems. Image compression is one of the key applications you can test your Unsupervised Machine Learning for data science.

In order to achieve a lossless compression, ML models need to be built using statistics that can capture clustering groups in the input distribution of data. Next, we need to develop a theoretical compression model that can be used to train machines to compress data in real-time. Then we can set the compression ratio based on the ‘model capacity’ which allows the data analyst to optimize ML probabilistic models in a highly dense data environment. Arithmetic coding and ANS models provide the best compression ratios, but your speed might be affected as we are still working on storage optimization and virtualization operations.

By using unsupervised learning, data science teams can unearth patterns and insights that were earlier undetected owning to its operation with unlabeled data. Compared to supervised ML models, unsupervised learning algorithms allow the data analysts to perform more complex procedures or tasks using Clustering, Anomaly Detection, and Convoluted Neural Networks, and NLPs.